

'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import GLPNImageProcessor






class      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  def __init__( self  :	Union[str, Any]   ,lowercase__  :	int   ,lowercase__  :	Any=7   ,lowercase__  :	Optional[Any]=3   ,lowercase__  :	Any=1_8   ,lowercase__  :	Optional[Any]=3_0   ,lowercase__  :	Any=4_0_0   ,lowercase__  :	str=True   ,lowercase__  :	Union[str, Any]=3_2   ,lowercase__  :	Tuple=True   ,):
   __lowercase				    =					parent
   __lowercase				    =					batch_size
   __lowercase				    =					num_channels
   __lowercase				    =					image_size
   __lowercase				    =					min_resolution
   __lowercase				    =					max_resolution
   __lowercase				    =					do_resize
   __lowercase				    =					size_divisor
   __lowercase				    =					do_rescale






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   return {
       "do_resize": self.do_resize,
       "size_divisor": self.size_divisor,
       "do_rescale": self.do_rescale,
   }






@require_torch
@require_vision
class      lowercase_      (lowerCamelCase__							,			unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Tuple							     =     GLPNImageProcessor if is_vision_available() else None






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					GLPNImageProcessingTester(self     )






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   return self.image_processor_tester.prepare_image_processor_dict()






  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase				    =					self.image_processing_class(**self.image_processor_dict     )
   self.assertTrue(hasattr(lowercase__   ,'''do_resize'''     )     )
   self.assertTrue(hasattr(lowercase__   ,'''size_divisor'''     )     )
   self.assertTrue(hasattr(lowercase__   ,'''resample'''     )     )
   self.assertTrue(hasattr(lowercase__   ,'''do_rescale'''     )     )






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   pass






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   # Initialize image_processing
   __lowercase				    =					self.image_processing_class(**self.image_processor_dict     )
   # create random PIL images
   __lowercase				    =					prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase__     )
   for image in image_inputs:
    self.assertIsInstance(lowercase__   ,Image.Image     )

   # Test not batched input (GLPNImageProcessor doesn't support batching)
   __lowercase				    =					image_processing(image_inputs[0]   ,return_tensors='''pt'''     ).pixel_values
   self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0     )
   self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   # Initialize image_processing
   __lowercase				    =					self.image_processing_class(**self.image_processor_dict     )
   # create random numpy tensors
   __lowercase				    =					prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase__   ,numpify=lowercase__     )
   for image in image_inputs:
    self.assertIsInstance(lowercase__   ,np.ndarray     )

   # Test not batched input (GLPNImageProcessor doesn't support batching)
   __lowercase				    =					image_processing(image_inputs[0]   ,return_tensors='''pt'''     ).pixel_values
   self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0     )
   self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   # Initialize image_processing
   __lowercase				    =					self.image_processing_class(**self.image_processor_dict     )
   # create random PyTorch tensors
   __lowercase				    =					prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase__   ,torchify=lowercase__     )
   for image in image_inputs:
    self.assertIsInstance(lowercase__   ,torch.Tensor     )

   # Test not batched input (GLPNImageProcessor doesn't support batching)
   __lowercase				    =					image_processing(image_inputs[0]   ,return_tensors='''pt'''     ).pixel_values
   self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0     )
   self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0     )





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							{
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Dict 			=		'trocr'
			_UpperCamelCase		:       Union[str, Any] 			=		['past_key_values']
			_UpperCamelCase		:       Union[str, Any] 			=		{
			    'num_attention_heads': 'decoder_attention_heads',
			    'hidden_size': 'd_model',
			    'num_hidden_layers': 'decoder_layers',
			}




			def __init__(				self   ,    snake_case=50265   ,    snake_case=1024   ,    snake_case=12   ,    snake_case=16   ,    snake_case=4096   ,    snake_case="gelu"   ,    snake_case=512   ,    snake_case=0.1   ,    snake_case=0.0   ,    snake_case=0.0   ,    snake_case=2   ,    snake_case=0.02   ,    snake_case=0.0   ,    snake_case=True   ,    snake_case=False   ,    snake_case=True   ,    snake_case=True   ,    snake_case=1   ,    snake_case=0   ,    snake_case=2   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							vocab_size
						UpperCamelCase__       =							d_model
						UpperCamelCase__       =							decoder_layers
						UpperCamelCase__       =							decoder_attention_heads
						UpperCamelCase__       =							decoder_ffn_dim
						UpperCamelCase__       =							activation_function
						UpperCamelCase__       =							max_position_embeddings
						UpperCamelCase__       =							dropout
						UpperCamelCase__       =							attention_dropout
						UpperCamelCase__       =							activation_dropout
						UpperCamelCase__       =							init_std
						UpperCamelCase__       =							decoder_layerdrop
						UpperCamelCase__       =							use_cache
						UpperCamelCase__       =							scale_embedding
						UpperCamelCase__       =							use_learned_position_embeddings
						UpperCamelCase__       =							layernorm_embedding

						super().__init__(
						    pad_token_id=snake_case   ,    bos_token_id=snake_case   ,    eos_token_id=snake_case   ,    decoder_start_token_id=snake_case   ,    **snake_case   ,    )


